Image matching technologies have come into widespread use in various fields. For example, technologies for matching images taken with visible light or infrared cameras to template images are used in the facial recognition and biometric authentication fields. In addition, technologies for matching images taken with camera mobile phones or digital cameras to landmark images are also used extensively.
There is an image matching method which, for example, compares a feature value of each feature point (which is hereinafter sometimes referred to as “local feature value”) in an input image with local feature values of feature points in a reference image to thereby search for a feature point in the reference image, corresponding to the feature point in the input image. Note that such a feature point in the reference image is termed “corresponding point” and a search for a corresponding point is termed “corresponding point search”. A set of corresponding points found through searches are statistically processed, which allows recognition of the presence or location of the reference image within the input image.
Image feature values, such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Binary Robust Independent Elementary Features (BRIEF), are used for corresponding point searches. For example, BRIEF is characterized by representing local features as sets of bit values (local feature values), each defined according to luminance differences of a plurality of pixel pairs distributed around each feature point. Oriented Fast and Rotated BRIEF (ORB) and Fast Retina Keypoint (FREAK) are examples of image feature values using luminance differences between pixels.
One method proposed to reduce processing load involved in such corresponding point searches is to reuse information on luminance differences calculated previously when image patches associated with a plurality of feature points and having a plurality of pixel pairs arranged therein overlap each other. Another proposed method is directed to integrating a plurality of feature data records calculated from a plurality of query images and outputting the integrated result. Still another proposed method is to speed up matching by narrowing down features detected in an image by eliminating those lying along edges and line contours. Yet another proposed method employs Features from Accelerated Segment Test (FAST) before BRIEF computation to thereby increase processing speed.
See, for example, the following documents:    Japanese Laid-open Patent Publication No. 2016-45837;    Japanese Laid-open Patent Publication No. 2013-101423;    Japanese Laid-open Patent Publication No. 2013-109773;    International Publication Pamphlet No. WO 2011021605;    David G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 60, 2 (2004), pp. 91-110;    H. Bay, T. Tuytelaars, and L. V. Gool, “SURF: Speed Up Robust Features”, Proceedings of the European Conference on Computer Vision (ECCV), 2006;    M. Calonder, V. Lepetit, C. Strecha, and P. Fua, “BRIEF: Binary Robust Independent Elementary Features”, In Proceedings of the European Conference on Computer Vision (ECCV), 2010;    Alexandre Alahi, Raphael Ortiz, Pierre Vandergheynst, “FREAK: Fast Retina Keypoint”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012;    E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An Efficient Alternative to SIFT or SURF”, In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2011; and
OpenCV: BRIEF (Binary Robust Independent Elementary Features), webpage, URL: docs.opencv.org/trunk/dc/d7d/tutorial_py_brief.html.
When corresponding point searches are implemented using local feature values based on signs associated with differences between pixels, such as BRIEF, erroneous matching tends to take place in flat and smooth image regions with minimal differences in colors and brightness and image regions including fine lines, for example. In a flat and smooth image region, for example, each sign associated with the difference between pixels may be easily changed due to the influence of noise or lighting variations. Also in an image region including fine lines, each sign associated with the difference between pixels would change depending on whether one of the paired pixels happens to lie on a fine line. Removing such unstable feature points likely to cause erroneous matching and selecting robust feature points less likely to produce incorrect matching offer a contribution to improved image recognition accuracy.